Radar and optical imaging are well known in the art and have been practiced for many years. Present day target imaging and identification systems are limited in the degree of resolution achievable because of constraints in aperture size, wavelength or frequencies which may be utilized to image a discrete object. Holographic imaging held the promise of high resolution of discrete objects by illumination of the object with coherent radiation and reception of the backscattered target data through a large aperture. This method has not proved practical or cost-effective however and does not produce three dimensional images of the target when the target is at a great distance. Since three dimensional imaging is the primary attractive feature of holography, the incentive for utilizing this type of system is diminished if this feature is lost. A typical holographic imaging system is disclosed in U.S. Pat. No. 3,766,533, Black et al. A system for accessing data in three dimensional storage is shown in U.S. Pat. No. 4,471,470, Swainson et al.
More advanced imaging systems have been developed to address the long felt need for high resolution imaging of dilute objects. Systems which operate in the millimeter and microwave regimes measure the amplitude and phase response of targets. These systems utilize spectral degrees of freedom e.g., multiple frequencies, angular diversity, and polarization diversity, to produce a highly resolved image while reducing the number of required "looks" at the target. Furthermore, these systems are "smart sensing" since they perform a certain degree of pre-processing which reduces the dimensionality of the raw data collected by discarding information that is not essential for recognition. For example, because of the nature of electromagnetic scattering a microwave diversity imaging system discards information about flat parts of the target that specularly reflect incident illumination away from the imaging aperture and retains information about edges, protrusions, and other characteristic detail that scatters the illumination broadly and is therefore captured by the imaging aperture. As a result of this scattering behavior the image obtained is automatically edge enhanced representing a "primal sketch" or caricature of the object. It is well appreciated by those with ordinary skill in the art that the generation of a primary sketch is frequently the first step needed in machine recognition. In addition to the above, the image is tomographic and centered in the field of view which are additional desirable features for machine recognition. There is a long-felt need in the art for a cost effective and reliable method to produce super-resolved images. Those with ordinary skill in the art understand that super-resolution refers to reconstructing images from available data with higher resolution than what is predicted by the classical Rayleigh resolution criteria or other similar criteria. By reducing the number of looks required to produce a super-resolved image the number of transmitters can be reduced or, in the alternative, the number of transmittances from a single transmitter may be reduced. This greatly reduces the cost of producing super-resolved images.
The goal of super-resolution is achieved with these types of systems by imaging over a small spectral band width and varying the aspect angle from which the target is viewed. Therefore, less looks at the target are taken which produces less amplitude and phase response information from the target. However, since many spectral degrees of freedom exist an image can be super-resolved with fewer looks at the target.
The sine qua non of super-resolution is that the data collected about the object be analytic (differentiable) over a domain in the complex plane. Typically, however, the signal to noise ratio of the data is quite low for real world targets. Therefore, the signal backscattered, from for example an aerospace target, cannot be said to be analytic over a large enough domain in complex space to produce a function which is susceptible to super-resolution. In a real sense then, systems which utilize spectral degrees of freedom cannot meaningfully be said to fulfill the long felt need for super-resolution of an image from a discrete source.
Techniques for automated recognition of targets exist which produce tomographic images of the targets. Target derived references provide a means for determining the phase of the target without using a fixed reference for a number of receiver and transmitter sets. By varying the aspect angles from which a target is viewed and viewing it with preferably one, rather than a plurality of receiver and transmitter sets, a tomographic image of the target with edge enhanced features can be created which is centered in the field of view of the receiver. The tomographic image will appear as if the target is directly overhead even though it is being viewed,. at various aspect angles. This target derived reference method of obtaining a target's image is useful in classifying an unknown target since the target's range profile is obtained from target derived reference data. The presence of scattering centers on the target causes peak responses in the waveform which produce an edge enhanced image of the target. The features herein described have been dramatically demonstrated by the inventor and his co-authors in a paper published in Radio Science, Vol. 19, No. 5 (1984). With a plurality of known targets stored in memory it is possible to compare the range profiles of the target to those of the known objects so that the target may be identified. The range profile graph of a target is known to those with ordinary skill in the art as the target's sinogram classifier. The sinogram classifier is a preferred embodiment of a data set's feature space classifier.
A system which performs substantially this function is disclosed in U.S. Pat. No. 4,470,048 in the name of Short, III. The invention of the Short III patent does not fulfill the long-felt need for robust, fast processing of data for classification of information such as targets against known data sets, however. It has been proposed to perform such processing in a parallel fashion. Numerous types of systems which perform parallel processing of data stored in content addressable memory (CAM) exist. Such systems are inherently faster than systems which perform bit-by-bit, serial comparisons of known data sets with input data. In general, these systems utilize associative type memories to classify and process incoming data. Associative memories correlate input data with the data itself in order to create a data array for parallel processing of the input data to achieve identification of the input data through comparison with learned data sets. Exemplary CAM architectures with associative memories are disclosed in U.S. Pat. No. 3,766,532, Liebel; U.S. Pat. No. 4,254,476, Burrows; U.S. Pat. No. 4,488,260, Cantarel. However, none of these patents disclose inventions which satisfy the long felt need for robust systems that can identify an unknown data input through comparison with a stored entity when only a small amount of information about the unidentified object is available as input.
Similarly, there is a long felt need in this art for a system which stores learned data sets of or about objects as tomographic images which are centered in the field of view and which are edge enhanced to preserve only the relevant information about the object's major scattering points.
Systems which process and store data optically are well known within the art. Exemplary systems are disclosed in U.S. Pat. No. 4,471,470 Swainson et al; U.S. Pat. No. 4,532,608 Wu; U.S. Pat. No. 4,559,643, Brogardh; U.S. Pat. No. 4,586,164 Eden; U.S. Pat. No. 4,612,666, King; U.S. Pat. No. 4,627,029, Wilson et al; U.S. Pat. No. 4,641,350, Bunn. None of these disclose utilization of CAM architecture or associative type memories for robust, parallel processing of data. In these prior efforts parallel processing is achieved, in part, due to massive interconnectivity of memory elements or active elements. Such interconnectivity is impractical in VLSI or Opto-chemical devices because of physical limitations and the inordinate amount of cross-talk which exists between the connections. Optical implementation of the interconnections between memory elements or active elements would eliminate cross-talk between interconnections and allow the system to perform parallel processing in a fast and efficient manner. A long felt but unfulfilled need in the art thus exists for an imaging and identification system which utilizes CAM architecture with associative memory for parallel processing and can be implemented optically.
Present day target imaging and identification systems are highly susceptible to failure if a memory element is damaged or malfunctions. There is therefore, a long felt need in the art for a system which is fault tolerant. A fault tolerant system would be able to function when a number of memory elements or active elements have failed. None of the CAM architectures which have been mentioned above satisfy the long felt need for such fault tolerant systems.